Summary We previously piloted the concept of a Connectivity Map (CMap), whereby genes, drugs and disease states are connected by virtue of common gene-expression signatures. Here, we report more than a 1,000-fold scale-up of the CMap as part of the NIH LINCS Consortium, made possible by a new, low-cost, high throughput reduced representation expression profiling method that we term L1000. We show that L1000 is highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts. We further show that the expanded CMap can be used to discover mechanism of action of small molecules, functionally annotate genetic variants of disease genes, and inform clinical trials. The 1.3 million L1000 profiles described here, as well as tools for their analysis, are available at https://clue.io.

2 SUMMARYWe previously piloted the concept of a Connectivity Map (CMap), whereby genes, drugs and disease states are connected by virtue of common gene-expression signatures. Here, we report more than a 1,000-fold scale-up of the CMap as part of the NIH LINCS Consortium, made possible by a new, low-cost, high throughput reduced representation expression profiling method that we term L1000. We show that L1000 is highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts. We further show that the expanded CMap can be used to discover mechanism of action of small molecules, functionally annotate genetic variants of disease genes, and inform clinical trials. The 1.3 million L1000 profiles described here, as well as tools for their analysis, are available at https://clue.io.

SUMMARYAlthough the value of proteomics has been demonstrated, cost and scale are typically prohibitive, and gene expression profiling remains dominant for characterizing cellular responses to perturbations. However, high-throughput sentinel assays provide an opportunity for proteomics to contribute at a meaningful scale. We present a systematic library resource (90 drugs 3 6 cell lines) of proteomic signatures that measure changes in the reduced-representation phosphoproteome (P100) and changes in epigenetic marks on histones (GCP). A majority of these drugs elicited reproducible signatures, but notable cell line- and assay-specific differences were observed. Using the “connectivity” framework, we compared signatures across cell types and integrated data across assays, including a transcriptional assay (L1000). Consistent connectivity among cell types revealed cellular responses that transcended lineage, and consistent connectivity among assays revealed unexpected associations between drugs. We further leveraged the resource against public data to formulate hypotheses for treatment of multiple myeloma and acute lymphocytic leukemia. This resource is publicly available at https://clue.io/proteomics.

Though the added value of proteomic measurements to gene expression profiling has been demonstrated, profiling of gene expression on its own remains the dominant means of understanding cellular responses to perturbation. Direct protein measurements are typically limited due to issues of cost and scale; however, the recent development of high-throughput, targeted sentinel mass spectrometry assays provides an opportunity for proteomics to contribute at a meaningful scale in high-value areas for drug development. To demonstrate the feasibility of a systematic and comprehensive library of perturbational proteomic signatures, we profiled 90 drugs (in triplicate) in six cell lines using two different proteomic assays -one measuring global changes of epigenetic marks on histone proteins and another measuring a set of peptides reporting on the phosphoproteome -for a total of more than 3,400 samples. This effort represents a first-of-its-kind resource for proteomics. The majority of tested drugs generated reproducible responses in both phosphosignaling and chromatin states, but we observed differences in the responses that were cell line-and assay-specific. We formalized the process of comparing response signatures within the data using a concept called connectivity, which enabled us to integrate data across cell types and assays. Furthermore, it facilitated incorporation of transcriptional signatures. Consistent connectivity among cell types revealed cellular responses that transcended cell-specific effects, while consistent connectivity among assays revealed unexpected associations between drugs that were confirmed by experimental follow-up. We further demonstrated how the resource could be leveraged against public domain external datasets to recognize therapeutic hypotheses that are consistent with ongoing clinical trials for the treatment of multiple myeloma and acute lymphocytic leukemia (ALL).

Our multiplexed cell viability platform, PRISM (profiling relative inhibition simultaneously in mixtures), enables screening of potential cancer therapeutics at an unprecedented scale. We routinely assess the effects of perturbations against more than 900 cancer cell lines concurrently through the use of unique oligonucleotide barcodes stably transduced into individual cancer cell lines. Following barcode transduction, individual cell lines are pooled together in groups of 20-25 based on growth rate similarity, then thawed into 384-well assay-ready plates containing compounds of interest. After 5 days of growth, isolated mRNA is used to detect transcribed barcode abundance of each individual cancer cell line to measure relative viability. We leverage the baseline cellular features (e.g., gene expression, cell lineage, mutation, copy number, metabolomics, proteomics, genome-wide RNAi and CRISPR dependencies) of each cell line to interpret viability profiles, enabling identification of drivers of differential sensitivity and potential biomarkers of compound response. Critically, the scale and throughput of PRISM has enabled the generation of large, publicly available datasets and the rapid characterization of emerging therapeutic targets and classes (e.g., isoform-selective RAS inhibitors and degraders). Although the read-out of PRISM is relative cell line viability, the platform can be used to answer a multitude of scientific questions. For example, PRISM data can be used to identify potential patient populations who would most benefit from treatment with a specific compound, uncover unexpected off-target toxicities, and validate mechanism of action hypotheses on a more holistic scale. Ultimately, PRISM offers a large-scale and comprehensive platform for the testing and validation of anti-cancer compounds to aid in the search for new oncology therapeutics. Citation Format: Ellen Nguyen, Shiker Nair, John Davis, Antonella Masciotti, Connor Mochi, John Finn, Cole Ponsi, Brienne Engel, Claudine Mapa, Mustafa Kocak, Melissa Ronan, Matthew G. Rees, Jennifer A. Roth. Identifying therapeutic mechanism of action and new potential patient populations using PRISM [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2748.